Horus: A WLAN-Based Indoor Location Determination System

1. H O R U S H O R U S Horus: A WLAN-Based Indoor Location Determination System Moustafa Youssef 2003

2. Motivation • Ubiquitous computing is increasingly popular • Requires • Context information: location, time, … • Connectivity: 802.11b, Bluetooth, … • Location-aware applications • Location-sensitive billing • Tourist services • Asset tracking • E911 • Security • …

3. Location Determination Technologies • GPS • Cellular-based • Ultrasonic-based: Active Bat • Infrared-based: Active Badge • Computer vision: Easy Living • Physical proximity: Smart Floor • Not suitable for indoor • Does not work • Require specialized hardware • Scalability

4. WLAN Location Determination • Triangulate user location • Reference point • Quantity proportional to distance • WLAN • Access points • Signal strength= f(distance) • Software based

5. Roadmap • Motivation • Location determination technologies • Introduction • Noisy wireless channel • Horus components • Performance evaluation • Conclusions and future work

6. WLAN Location Determination (Cont’d) • Signal strength= f(distance) • Does not follow free space loss • Use lookup table  Radio map • Radio Map: signal strength characteristics at selected locations

7. (xi, yi) (x, y) WLAN Location Determination (Cont’d) • Offline phase • Build radio map • Radar system: average signal strength • Online phase • Get user location • Nearest location in signal strength space (Euclidian distance) [-50, -60] 5 [-53, -56] 13 [-58, -68]

8. WLAN Location Determination Taxonomy

9. Horus Goals • High accuracy • Wider range of applications • Energy efficiency • Energy constrained devices • Scalability • Number of supported users • Coverage area

10. Contributions • Taxonomy of WLAN location determination systems • Modeling the signal strength distributions using parametric and non-parametric distributions • Handling correlation between successive samples from the same access point • Allowing continuous space estimation • Clustering of radio map locations • Handling small-scale variations • Compare the performance of the Horus system with other systems

11. Roadio-map • Motivation • Location determination technologies • Introduction • Noisy wireless channel • Horus components • Performance evaluation • Conclusions and future work

12. Sampling Process • Active scanning • Send a probe request • Receive a probe response

13. Signal Strength Characteristics • Temporal variations • One access point • Multiple access points • Spatial variations • Large scale • Small scale

14. Temporal Variations

15. Temporal Variations

16. Temporal Variations:Correlation

17. Spatial Variations: Large-Scale

18. Spatial Variations: Small-Scale

19. Roadio-map • Motivation • Goals • Introduction • Noisy wireless channel • Horus components • Performance evaluation • Conclusions and future work

20. Testbeds • A.V. William’s • 4th floor, AVW • 224 feet by 85.1 feet • UMD net (Cisco APs) • 21 APs (6 on avg.) • 172 locations • 5 feet apart • Windows XP Prof. • FLA • 3rd floor, 8400 Baltimore Ave • 39 feet by 118 feet • LinkSys/Cisco APs • 6 APs (4 on avg.) • 110 locations • 7 feet apart • Linux (kernel 2.5.7) Orinoco/Compaq cards

21. Horus Components • Basic algorithm [Percom03] • Correlation handler [InfoCom04] • Continuous space estimator [Under] • Locations clustering [Percom03] • Small-scale compensator [WCNC03]

22. Basic Algorithm:Mathematical Formulation • x: Position vector • s: Signal strength vector • One entry for each access point • s(x) is a stochastic process • P[s(x), t]: probability of receiving s at x at time t • s(x) is a stationary process • P[s(x)] is the histogram of signal strength at x

23. Basic Algorithm:Mathematical Formulation

24. Basic Algorithm:Mathematical Formulation • Argmaxx[P(x/s)] • Using Bayesian inversion • Argmaxx[P(s/x).P(x)/P(s)] • Argmaxx[P(s/x).P(x)] • P(x): User history

25. Basic Algorithm • Offline phase • Radio map: signal strength histograms • Online phase • Bayesian based inference

26. (xi, yi) (x, y) WLAN Location Determination (Cont’d) -40 -60 -80 P(-53/L1)=0.55 [-53] P(-53/L2)=0.08 -40 -60 -80

27. Basic Algorithm: Signal Strength Distributions

28. Basic Algorithm: Results • Accuracy of 5 feet 90% of the time • Slight advantage of parametric over non-parametric method • Smoothing of distribution shape

29. Correlation Handler • Need to average multiple samples to increase accuracy • Independence assumption is wrong

30. Correlation Handler:Autoregressive Model • s(t+1)=.s(t)+(1- ).v(t) • : correlation degree • E[v(t)]=E[s(t)] • Var[v(t)]= (1+ )/(1- ) Var[s(t)]

31. Correlation Handler: Averaging Process • s(t+1)= .s(t)+(1- ).v(t) • s ~ N(0, m) • v ~ N(0, r) • A=1/n (s1+s2+...+sn) • E[A(t)]=E[s(t)]=0 • Var[A(t)]= m2/n2 { [(1- n)/(1- )]2 + n+ 1- 2 *(1- 2(n-1))/(1- 2) }

32. Correlation Handler: Averaging

33. Correlation Handler: Results • Independence assumption: performance degrades as n increases • Two factors affecting accuracy • Increasing n • Deviation from the actual distribution

34. Continuous Space Estimator • Enhance the discrete radio map space estimator • Two techniques • Center of mass of the top ranked locations • Time averaging window

35. Center of Mass: Results • N = 1 is the discrete-space estimator • Accuracy enhanced by more than 13%

36. Time Averaging Window: Results • N = 1 is the discrete-space estimator • Accuracy enhanced by more than 24%

37. Horus Components • Basic algorithm • Correlation handler • Continuous space estimator • Small-scale compensator • Locations clustering

38. Small-scale Compensator • Multi-path effect • Hard to capture by radio map (size/time)

39. Small-scale Compensator: Small-scale Variations AP1 AP2 • Variations up to 10 dBm in 3 inches • Variations proportional to average signal strength

40. Small-scale Compensator:Perturbation Technique • Detect small-scale variations • Using previous user location • Perturb signal strength vector • (s1, s2, …, sn)  (s1d1, s2d2, …, sndn) • Typically, n=3-4 • di is chosen relative to the received signal strength

41. Small-scale Compensator: Results • Perturbation technique is not sensitive to the number of APs perturbed • Better by more than 25%

42. Horus Components • Basic algorithm • Correlation handler • Continuous space estimator • Small-scale compensator • Locations clustering

43. Locations Clustering • Reduce computational requirements • Two techniques • Explicit • Implicit

44. S=[-45, -60, -70, -80, -86] q=3 Locations Clustering: Explicit Clustering • Use access points that cover each location • Use the q strongest access points S=[-60, -45, -80, -86, -70]

45. Locations Clustering:Results- Explicit Clustering • An order of magnitude enhancement in avg. num. of oper. /location estimate • As q increases, accuracy slightly increases

46. Locations Clustering: Implicit Clustering • Use the access points incrementally • Implicit multi-level clustering S=[-60, -45, -80, -86, -70] S=[-45, -60, -70, -80, -86] S=(-45, -60, -70, -80, -86)

47. Locations Clustering:Results- Implicit Clustering • Avg. num. of oper. /location estimate better than explicit clustering • Accuracy increases with Threshold

48. Horus Components

49. Roadio-map • Motivation • Location Determination technologies • Introduction • Noisy wireless channel • Horus components • Performance evaluation • Conclusions and future work

50. Horus-Radar Comparison